Open Access   Article Go Back

Cost Effective PSO Model for MapReduce in Cloud Environment

Vidhyasagar B S1 , Ajithkumar M2 , Shaik Sajid3 , Syed Khadeer4 , Rahul P5 , J. Arunnehru6

  1. Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
  2. Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
  3. Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
  4. Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
  5. Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
  6. Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-4 , Page no. 497-501, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i4.497501

Online published on Apr 30, 2018

Copyright © Vidhyasagar B S, Ajithkumar M, Shaik Sajid, Syed Khadeer , Rahul P, J. Arunnehru . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Vidhyasagar B S, Ajithkumar M, Shaik Sajid, Syed Khadeer , Rahul P, J. Arunnehru, “Cost Effective PSO Model for MapReduce in Cloud Environment,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.497-501, 2018.

MLA Style Citation: Vidhyasagar B S, Ajithkumar M, Shaik Sajid, Syed Khadeer , Rahul P, J. Arunnehru "Cost Effective PSO Model for MapReduce in Cloud Environment." International Journal of Computer Sciences and Engineering 6.4 (2018): 497-501.

APA Style Citation: Vidhyasagar B S, Ajithkumar M, Shaik Sajid, Syed Khadeer , Rahul P, J. Arunnehru, (2018). Cost Effective PSO Model for MapReduce in Cloud Environment. International Journal of Computer Sciences and Engineering, 6(4), 497-501.

BibTex Style Citation:
@article{S_2018,
author = {Vidhyasagar B S, Ajithkumar M, Shaik Sajid, Syed Khadeer , Rahul P, J. Arunnehru},
title = {Cost Effective PSO Model for MapReduce in Cloud Environment},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2018},
volume = {6},
Issue = {4},
month = {4},
year = {2018},
issn = {2347-2693},
pages = {497-501},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1927},
doi = {https://doi.org/10.26438/ijcse/v6i4.497501}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i4.497501}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1927
TI - Cost Effective PSO Model for MapReduce in Cloud Environment
T2 - International Journal of Computer Sciences and Engineering
AU - Vidhyasagar B S, Ajithkumar M, Shaik Sajid, Syed Khadeer , Rahul P, J. Arunnehru
PY - 2018
DA - 2018/04/30
PB - IJCSE, Indore, INDIA
SP - 497-501
IS - 4
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
518 321 downloads 229 downloads
  
  
           

Abstract

Cloud service provides everything as a service over the Internet or Intranet. Provisioning and allocation of virtual resource over the network requests based on used demand (pay-as-you-go). Big Data, which has large set of data that are so voluminous and complex that traditional method is not enough to process the data, Hadoop MapReduce framework is used to process the large set of data in a distributed manner. Efficient slave nodes selection is difficult to setup Hadoop cluster in cloud environment which led to more cost. We have proposed an algorithm called Particle Swarm Optimization(PSO) that determines the optimal number of nodes in the Hadoop cluster utilizes based on the data sets which provides efficient job execution on minimal set of DataNodes in cloud environment.

Key-Words / Index Term

Hadoop, MapReduce, Virtualization, PSO,YARN, HDFS

References

[1]. Selvaprabhu, “Fragile data Storing in public cloud for hospital administration”2017 14th, VOL 5, NO14, “IEEE International Conference on Services Computing”.
[2]. .AniketMalatpure,”Testing Private Cloud Reliability Using a Public CloudValidation SaaS”,2017,IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).
[3]. .Joonseok Park, “Pattern-based Cloud Service Recommendation and Integration for Hybrid Cloud”,Research Institute of Logistics Innovation, Volume 41, Number 1, January 2011.
[4]. Mukhtaj Khan, Yong Jin, Maozhen Li, Yang Xiang, and Changjun Jiang, “Hadoop Performance Modeling for Job Estimation and Resource Provisioning”, FEBRUARY 2016, NO.2, VOL. 27, “IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS”.
[5]. Tzu-Chi Huang, Kuo-Chih Chu, Yu-Ruei Rao, “Smart Intermediate Data Transfer for MapReduce on Cloud Computing”, Volume 1 Issue 4, 2011, “International Conference on Cloud Computing and Big Data”.
[6]. R.Thangaselvi, Ananthbabu, Aruna, Jagadeesh,” Improving the efficiency ofMapReduce scheduling algorithm in Hadoop”, Number 1, Volume 19, “IEEE ComputerSociety”.
[7]. J. Dean and S. Ghemawat, “MapReduce: Simplified data process-ing on large clusters,” in Proc. 6th Symp. Operating Syst. Des. Imple-mentation, 2004, p. 10.
[8]. G. Ananthanarayanan, S. Kandula, A. Greenberg, I. Stoica, Y. Lu, B. Saha, and E. Harris, “Reining in the outliers in Map-Reduce clusters using Mantri,” in Proc. 9th USENIX Conf. Operating Syst. Des. Implementation, 2010.
[9]. Dr. (Mrs.) Ananthi Sheshasaayee ,“ A Theoretical Framework for Cloud Resource Provisioning using MapReduce Technique “PG and Research Department of Computer Science&Application.
[10]. K. Kambatla, A. Pathak, and H. Pucha, “Towards optimizing Hadoop provisioning in the cloud,” in Proc. Conf. Hot Topics Cloud Comput., 2009.
[11]. Amol C. Adamuthe,”Solving Resource Provisioning in Cloud using GAs andPSO”,Dept. of CSE, RIT, Rajaramnagar-Islampur, MS, India ,2013 Nirma University International Conference on Engineering (NUiCONE).
[12]. Balaji Palanisamy, “Cost-Effective Resource Provisioning for MapReduce in a Cloud”, IEEETRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 26, NO 5, MAY 2015.
[13]. Bramantyo Adrian, “Analysis of K-means Algorithm For VM Allocation in Cloud Computing”, Yogyakarta, Indonesia, International Conference on Data and Software Engineering, 2015.
[14]. Clayton Maciel Costa, “Service Response Time Measurement Model of Service Level Agreements in Cloud environment”, IEEE International Conference on Smart City, SocialCom together with DataCom, 2015.
[15]. Yongmei WEI, “A cost-effective and reliable cloud storage“, Nanyang Polytechnic Singapore, IEEE International Conference on Cloud Computing, 2014.
[16]. P.Varalakshmi, Maheshwari.K, “Cost-Optimized Resource Provisioning in Cloud“, Department of Information Technology, Anna University-MIT, International Conference on Recent Trends in Information Technology (ICRTIT), 2013.
[17]. Mahesh B. Nagpure,”An Efficient Dynamic Resource Allocation Strategy for VM Environment in Cloud”, Student M. tech 2nd year, Dept of Computer Science &Engineering, 2015 International Conference on Pervasive Computing (ICPC).
[18]. Vivek Rajani, “A VM Allocation Strategy for Cluster of Open Host in Cloud Environment”, Research Scholar ITSNS- GTU PG SCHOOL, 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).
[19]. Berl, A., Gelenbe,“Energy-Efficient Cloud Computing”, The Computer Journal August 19 2009.
[20]. Vidhyasagar. B. S., S. Aravinda Krishnan, D. Manikkannan, and J. Arunnehru. "An Implementation and Performance Monitoring of Virtual Machines using Ganglia in Eucalyptus Private Cloud." International Journal on Computer Science and Engineering (IJCSE), 2017